The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
GMM-UBM 프레임워크는 최근 ASV(자동 화자 확인) 작업에 대한 가장 효과적인 접근 방식 중 하나로 입증되었습니다. 이 편지에서 우리는 먼저 전통적인 GMM-UBM의 대략적인 결정 기능을 제안하며, 이를 통해 각 가우스 구성 요소의 분류에 대한 기여도가 똑같이 중요하다는 것을 보여줍니다. 그러나 화자 인식에 대한 연구에 따르면 가우시안 구성 요소로 정의된 서로 다른 음성 사운드 단위가 화자 확인에 서로 다른 기여를 한다는 것을 보여줍니다. 이는 화자 검증을 위한 정보가 거의 포함되지 않은 음성 사운드 단위를 덜 강조하면서 화자 간 식별이 가능한 일부 사운드 단위를 강조하도록 동기를 부여합니다. 2006 NIST SRE 핵심 작업에 대한 실험에서는 제안된 접근 방식이 분류 정확도 측면에서 기존 GMM-UBM 접근 방식보다 우수한 것으로 나타났습니다.
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부
Xiang XIAO, Xiang ZHANG, Haipeng WANG, Hongbin SUO, Qingwei ZHAO, Yonghong YAN, "Approximate Decision Function and Optimization for GMM-UBM Based Speaker Verification" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 9, pp. 1798-1802, September 2009, doi: 10.1587/transinf.E92.D.1798.
Abstract: The GMM-UBM framework has been proved to be one of the most effective approaches to the automatic speaker verification (ASV) task in recent years. In this letter, we first propose an approximate decision function of traditional GMM-UBM, from which it is shown that the contribution to classification of each Gaussian component is equally important. However, research in speaker perception shows that a different speech sound unit defined by Gaussian component makes a different contribution to speaker verification. This motivates us to emphasize some sound units which have discriminability between speakers while de-emphasize the speech sound units which contain little information for speaker verification. Experiments on 2006 NIST SRE core task show that the proposed approach outperforms traditional GMM-UBM approach in classification accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1798/_p
부
@ARTICLE{e92-d_9_1798,
author={Xiang XIAO, Xiang ZHANG, Haipeng WANG, Hongbin SUO, Qingwei ZHAO, Yonghong YAN, },
journal={IEICE TRANSACTIONS on Information},
title={Approximate Decision Function and Optimization for GMM-UBM Based Speaker Verification},
year={2009},
volume={E92-D},
number={9},
pages={1798-1802},
abstract={The GMM-UBM framework has been proved to be one of the most effective approaches to the automatic speaker verification (ASV) task in recent years. In this letter, we first propose an approximate decision function of traditional GMM-UBM, from which it is shown that the contribution to classification of each Gaussian component is equally important. However, research in speaker perception shows that a different speech sound unit defined by Gaussian component makes a different contribution to speaker verification. This motivates us to emphasize some sound units which have discriminability between speakers while de-emphasize the speech sound units which contain little information for speaker verification. Experiments on 2006 NIST SRE core task show that the proposed approach outperforms traditional GMM-UBM approach in classification accuracy.},
keywords={},
doi={10.1587/transinf.E92.D.1798},
ISSN={1745-1361},
month={September},}
부
TY - JOUR
TI - Approximate Decision Function and Optimization for GMM-UBM Based Speaker Verification
T2 - IEICE TRANSACTIONS on Information
SP - 1798
EP - 1802
AU - Xiang XIAO
AU - Xiang ZHANG
AU - Haipeng WANG
AU - Hongbin SUO
AU - Qingwei ZHAO
AU - Yonghong YAN
PY - 2009
DO - 10.1587/transinf.E92.D.1798
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2009
AB - The GMM-UBM framework has been proved to be one of the most effective approaches to the automatic speaker verification (ASV) task in recent years. In this letter, we first propose an approximate decision function of traditional GMM-UBM, from which it is shown that the contribution to classification of each Gaussian component is equally important. However, research in speaker perception shows that a different speech sound unit defined by Gaussian component makes a different contribution to speaker verification. This motivates us to emphasize some sound units which have discriminability between speakers while de-emphasize the speech sound units which contain little information for speaker verification. Experiments on 2006 NIST SRE core task show that the proposed approach outperforms traditional GMM-UBM approach in classification accuracy.
ER -